Computational modelling of anti-angiogenic therapies based on multiparametric molecular imaging data
Benjamin Titz1, Kevin R Kozak, Robert Jeraj
1Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. titz@wisc.edu
Abstract:
Computational tumour models have emerged as powerful tools for the optimization of cancer therapies; ideally, these models should incorporate patient-specific imaging data indicative of therapeutic response. The purpose of this study was to develop a tumour modelling framework in order to simulate the therapeutic effects of anti-angiogenic agents based upon clinical molecular imaging data. The model was applied to positron emission tomography (PET) data of cellular proliferation and hypoxia from a phase I clinical trial of bevacizumab, an antibody that neutralizes the vascular endothelial growth factor (VEGF). When using pre-therapy PET data in combination with literature-based dose response parameters, simulated follow-up hypoxia data yielded good qualitative agreement with imaged hypoxia levels. Improving the quantitative agreement with follow-up hypoxia and proliferation PET data required tuning of the maximum vascular growth fraction (VGF(max)) and the tumour cell cycle time to patient-specific values. VGF(max) was found to be the most sensitive model parameter (CV = 22%). Assuming availability of patient-specific, intratumoural VEGF levels, we show how bevacizumab dose levels can potentially be 'tailored' to improve levels of tumour hypoxia while maintaining proliferative response, both of which are critically important in the context of combination therapy. Our results suggest that, upon further validation, the application of image-driven computational models may afford opportunities to optimize dosing regimens and combination therapies in a patient-specific manner.
Insights
Computational tumor models integrating patient imaging data can optimize cancer therapies. This study simulated anti-angiogenic drug effects, showing potential for personalized treatment strategies.
Area of Science:
- Computational biology
- Medical imaging
- Pharmacology
Background:
- Computational tumor models are crucial for optimizing cancer therapies.
- Integrating patient-specific imaging data enhances model accuracy for predicting therapeutic response.
Purpose of the Study:
- To develop a computational tumor modeling framework for simulating anti-angiogenic agent effects.
- To utilize clinical molecular imaging data, specifically positron emission tomography (PET), for model input.
Main Methods:
- Applied a computational model to PET data of cellular proliferation and hypoxia from a bevacizumab Phase I clinical trial.
- Used pre-therapy PET data and literature-based parameters to simulate follow-up hypoxia.
- Tuned model parameters, including maximum vascular growth fraction (VGFmax) and tumor cell cycle time, to patient-specific values for improved quantitative agreement.
Main Results:
- Simulated hypoxia data showed good qualitative agreement with imaged levels using pre-therapy PET data.
- Quantitative agreement improved with patient-specific tuning of VGFmax and cell cycle time.
- VGFmax was identified as the most sensitive model parameter (CV = 22%).
Conclusions:
- Image-driven computational models can simulate therapeutic effects of anti-angiogenic agents like bevacizumab.
- Patient-specific model parameter tuning is essential for accurate quantitative predictions.
- These models offer potential for optimizing dosing and combination therapies in a personalized manner upon further validation.
